def generate_test_bench(self, count_file_path, **kwargs):
preserve_columns = kwargs['preserve_columns']
count_file_path = os.path.abspath(count_file_path)
count_rna = self.data_set.get("RNA")
count_adt = self.data_set.get("ADT")
# Shuffle columns
count_rna, original_columns, column_permutation = \
shuffle_and_rename_columns(count_rna, disabled=preserve_columns)
# Remove zero rows
count_rna = count_rna[np.sum(count_rna, axis=1) > 0].copy()
# Save hidden data
make_sure_dir_exists(settings.STORAGE_DIR)
hidden_data_file_path = os.path.join(settings.STORAGE_DIR,
"%s.hidden.pkl.gz" % self.uid)
dump_gzip_pickle([
count_rna.to_sparse(), original_columns, column_permutation,
count_adt.to_sparse(), self.protein_rna_mapping
], hidden_data_file_path)
log("Benchmark hidden data saved to `%s`" % hidden_data_file_path)
make_sure_dir_exists(os.path.dirname(count_file_path))
write_csv(count_rna, count_file_path)
log("Count file saved to `%s`" % count_file_path)
def generate_test_bench(self, count_file_path, **kwargs):
preserve_columns = kwargs['preserve_columns']
count_file_path = os.path.abspath(count_file_path)
count_matrix, classes = self._load_data()
# Remove zero rows
count_matrix = count_matrix[np.sum(count_matrix, axis=1) > 0].copy()
# Shuffle columns
count_matrix, original_columns, column_permutation = \
shuffle_and_rename_columns(count_matrix, disabled=preserve_columns)
# Save hidden data
make_sure_dir_exists(settings.STORAGE_DIR)
hidden_data_file_path = os.path.join(settings.STORAGE_DIR,
"%s.hidden.pkl.gz" % self.uid)
dump_gzip_pickle([
count_matrix.to_sparse(), classes, original_columns,
column_permutation
], hidden_data_file_path)
log("Benchmark hidden data saved to `%s`" % hidden_data_file_path)
make_sure_dir_exists(os.path.dirname(count_file_path))
write_csv(count_matrix, count_file_path)
log("Count file saved to `%s`" % count_file_path)
def generate_test_bench(self, count_file_path, **kwargs):
n_samples = kwargs['n_samples']
dropout_count = kwargs['dropout_count']
min_expression = kwargs['min_expression']
hvg_frac = kwargs['hvg_frac']
preserve_columns = kwargs['preserve_columns']
count_file_path = os.path.abspath(count_file_path)
data = self._load_data(n_samples)
hvg_indices = self.get_hvg_genes(data, hvg_frac)
# Generate elimination mask
non_zero_locations = []
data_values = data.values
for x in hvg_indices:
for y in range(data.shape[1]):
if data_values[x, y] >= min_expression:
non_zero_locations.append((x, y))
del data_values
mask = np.zeros_like(data)
masked_locations = [
non_zero_locations[index] for index in np.random.choice(
len(non_zero_locations), dropout_count, replace=False)
]
for (x, y) in masked_locations:
mask[x, y] = 1
mask = pd.DataFrame(mask, index=data.index, columns=data.columns)
# Elimination
low_quality_data = data * (1 - mask.values)
is_nonzero = np.sum(low_quality_data, axis=1) > 0
mask = mask[is_nonzero].copy()
data = data[is_nonzero].copy()
low_quality_data = low_quality_data[is_nonzero].copy()
# Shuffle columns
low_quality_data, original_columns, column_permutation = \
shuffle_and_rename_columns(low_quality_data, disabled=preserve_columns)
# Save hidden data
make_sure_dir_exists(settings.STORAGE_DIR)
hidden_data_file_path = os.path.join(settings.STORAGE_DIR,
"%s.hidden.pkl.gz" % self.uid)
dump_gzip_pickle([
data.to_sparse(),
mask.to_sparse(), original_columns, column_permutation
], hidden_data_file_path)
log("Benchmark hidden data saved to `%s`" % hidden_data_file_path)
make_sure_dir_exists(os.path.dirname(count_file_path))
write_csv(low_quality_data, count_file_path)
log("Count file saved to `%s`" % count_file_path)
def generate_test_bench(self, count_file_path, **kwargs):
n_samples = kwargs['n_samples']
read_ratio = kwargs['read_ratio']
replce = kwargs['replace']
preserve_columns = kwargs['preserve_columns']
count_file_path = os.path.abspath(count_file_path)
data = self._load_data(n_samples)
# find cumulative distribution (sum)
data_values = data.astype(int).values
n_all_reads = np.sum(data_values)
data_cumsum = np.reshape(np.cumsum(data_values), data_values.shape)
# Sample from original dataset
new_reads = np.sort(
np.random.choice(n_all_reads,
int(read_ratio * n_all_reads),
replace=replce))
low_quality_data = np.zeros_like(data_values)
read_index = 0
for x in range(data_values.shape[0]):
for y in range(data_values.shape[1]):
while read_index < len(
new_reads) and new_reads[read_index] < data_cumsum[x,
y]:
low_quality_data[x, y] += 1
read_index += 1
# Convert to data frame
low_quality_data = pd.DataFrame(low_quality_data,
index=data.index,
columns=data.columns)
# Shuffle columns
low_quality_data, original_columns, column_permutation = \
shuffle_and_rename_columns(low_quality_data, disabled=preserve_columns)
# Remove zero rows
data = data[np.sum(low_quality_data, axis=1) > 0].copy()
low_quality_data = low_quality_data[
np.sum(low_quality_data, axis=1) > 0].copy()
# Save hidden data
make_sure_dir_exists(settings.STORAGE_DIR)
hidden_data_file_path = os.path.join(settings.STORAGE_DIR,
"%s.hidden.pkl.gz" % self.uid)
dump_gzip_pickle([
data.to_sparse(), read_ratio, original_columns, column_permutation
], hidden_data_file_path)
log("Benchmark hidden data saved to `%s`" % hidden_data_file_path)
make_sure_dir_exists(os.path.dirname(count_file_path))
write_csv(low_quality_data, count_file_path)
log("Count file saved to `%s`" % count_file_path)
def generate_test_bench(self, count_file_path, **kwargs):
count_file_path = os.path.abspath(count_file_path)
rm_ercc = kwargs['rm_ercc']
rm_mt = kwargs['rm_mt']
rm_lq = kwargs['rm_lq']
preserve_columns = kwargs['preserve_columns']
# Load dataset
data = self._load_and_combine_data()
# Remove some rows and columns
if rm_ercc:
remove_list = [
symbol for symbol in data.index.values
if symbol.startswith("ERCC-")
]
data = data.drop(remove_list)
if rm_mt:
remove_list = [
symbol for symbol in data.index.values
if symbol.startswith("mt-")
]
data = data.drop(remove_list)
if rm_lq:
remove_list = data.columns.values[data.sum(axis=0) < 1e6]
data = data.drop(columns=remove_list)
# Remove empty rows
remove_list = data.index.values[data.sum(axis=1) == 0]
data = data.drop(remove_list)
# Shuffle columns
new_data, original_columns, column_permutation = shuffle_and_rename_columns(
data, disabled=preserve_columns)
# Save hidden data
make_sure_dir_exists(settings.STORAGE_DIR)
hidden_data_file_path = os.path.join(settings.STORAGE_DIR,
"%s.hidden.pkl.gz" % self.uid)
dump_gzip_pickle(
[data.to_sparse(), original_columns, column_permutation],
hidden_data_file_path)
log("Benchmark hidden data saved to `%s`" % hidden_data_file_path)
make_sure_dir_exists(os.path.dirname(count_file_path))
write_csv(new_data, count_file_path)
log("Count file saved to `%s`" % count_file_path)
return None
def evaluate_result(self, processed_count_file_path, result_dir,
visualization, **kwargs):
normalization = kwargs['normalization']
transformation = kwargs['transformation']
clear_cache = kwargs['clear_cache']
make_sure_dir_exists(os.path.join(result_dir, "files"))
info = []
# Load hidden state and data
count_matrix, classes, original_columns, column_permutation = self._load_hidden_state(
)
# Load imputed data
imputed_data = read_table_file(processed_count_file_path)
# Restore column names and order
imputed_data = rearrange_and_rename_columns(imputed_data,
original_columns,
column_permutation)
# Data transformations
if np.sum(imputed_data.values < 0) > 0:
log("Observed some negative values!")
imputed_data[imputed_data < 0] = 0
imputed_data = transformations[transformation](
normalizations[normalization](imputed_data))
# Save class details for future
write_csv(classes, os.path.join(result_dir, "files", "classes.csv"))
# Evaluation
metric_results = dict()
embedded_data_file_path = os.path.join(result_dir, "files",
"embedded_data.pkl.gz")
if os.path.exists(embedded_data_file_path) and not clear_cache:
embedded_data = load_gzip_pickle(embedded_data_file_path)
else:
embedded_data = self._get_embeddings(imputed_data)
dump_gzip_pickle(embedded_data, embedded_data_file_path)
log("Evaluating ...")
for class_label in classes.index.values:
class_names = classes.loc[class_label].values
for embedding_name in embedded_data:
emb, emb_2d = embedded_data[embedding_name]
embedding_slug = embedding_name.replace(" ", "_").lower()
k_means = KMeans(n_clusters=len(set(class_names)))
k_means.fit(emb)
clusters = k_means.predict(emb)
embedding_df = pd.DataFrame(emb)
embedding_df["X"] = emb_2d[:, 0]
embedding_df["Y"] = emb_2d[:, 1]
embedding_df["class"] = class_names
embedding_df["k_means_clusters"] = clusters
write_csv(
embedding_df,
os.path.join(result_dir, "files",
"%s_%s.csv" % (class_label, embedding_slug)))
info.append({
'filename':
"%s_%s.csv" % (class_label, embedding_slug),
'description':
'%s embedding of cells along %s labels' %
(embedding_name, class_label),
'plot_description':
'%s embedding of cells along %s labels (Classes can be identified '
'with their colors and K-means clusters are marked '
'with different shapes)' % (embedding_name, class_label),
})
metric_results.update({
'kmeans_on_%s_%s_adjusted_mutual_info_score' % (embedding_slug, class_label):
adjusted_mutual_info_score(class_names,
clusters,
average_method="arithmetic"),
'kmeans_on_%s_%s_v_measure_score' % (embedding_slug, class_label):
v_measure_score(class_names, clusters),
'embedding_%s_%s_calinski_harabaz_score' % (embedding_slug, class_label):
calinski_harabaz_score(emb, class_names),
'embedding_%s_%s_silhouette_score' % (embedding_slug, class_label):
silhouette_score(emb, class_names)
})
write_csv(pd.DataFrame(info),
os.path.join(result_dir, "files", "info.csv"))
result_path = os.path.join(result_dir, "result.txt")
with open(result_path, 'w') as file:
file.write("## METRICS:\n")
for metric in sorted(metric_results):
file.write("%s\t%4f\n" % (metric, metric_results[metric]))
file.write("##\n## ADDITIONAL INFO:\n")
log("Evaluation results saved to `%s`" % result_path)
if visualization != "none":
self.visualize_result(result_dir, output_type=visualization)
return metric_results
def evaluate_result(self, processed_count_file, result_dir, visualization,
**kwargs):
normalization = kwargs['normalization']
transformation = kwargs['transformation']
clear_cache = kwargs['clear_cache']
make_sure_dir_exists(os.path.join(result_dir, "files"))
info = []
data, imputed_data = self._load_data_and_imputed_data_for_evaluation(
processed_count_file)
gold_standard_classes = [
column_name.split("_")[0] for column_name in data.columns.values
]
# Data transformations
if np.sum(imputed_data.values < 0) > 0:
log("Observed some negative values!")
imputed_data[imputed_data < 0] = 0
imputed_data = transformations[transformation](
normalizations[normalization](imputed_data))
G1_S_related_part_of_imputed_data, G2_M_related_part_of_imputed_data = self._get_related_part(
imputed_data)
related_part_of_imputed_data = pd.concat([
G1_S_related_part_of_imputed_data,
G2_M_related_part_of_imputed_data
])
write_csv(
G1_S_related_part_of_imputed_data,
os.path.join(result_dir, "files",
"G1_S_related_part_of_imputed_data.csv"))
info.append({
'filename': 'G1_S_related_part_of_imputed_data.csv',
'description': 'Vales of genes related to G1/S',
'plot_description': 'Heatmap of Genes related to G1/S',
})
write_csv(
G2_M_related_part_of_imputed_data,
os.path.join(result_dir, "files",
"G2_M_related_part_of_imputed_data.csv"))
info.append({
'filename': 'G2_M_related_part_of_imputed_data.csv',
'description': 'Vales of genes related to G2/M',
'plot_description': 'Heatmap of Genes related to G2/M',
})
svm_results, knn_results = self._get_classification_results(
related_part_of_imputed_data, gold_standard_classes)
embedded_data_file_path = os.path.join(result_dir, "files",
"embedded_data.pkl.gz")
if os.path.exists(embedded_data_file_path) and not clear_cache:
embedded_data = load_gzip_pickle(embedded_data_file_path)
else:
embedded_data = self._get_embeddings(related_part_of_imputed_data)
dump_gzip_pickle(embedded_data, embedded_data_file_path)
metric_results = {
"classification_svm_mean_accuracy": np.mean(svm_results),
"classification_knn_mean_accuracy": np.mean(knn_results)
}
embedded_data["identity"] = related_part_of_imputed_data.transpose()
for i, embedding_name in enumerate(embedded_data):
emb = embedded_data[embedding_name]
k_means = KMeans(n_clusters=3)
k_means.fit(emb)
clusters = k_means.predict(emb)
embedding_slug = embedding_name.replace(" ", "_").lower()
if embedding_name != "identity":
embedding_df = pd.DataFrame(
{
"X": emb[:, 0],
"Y": emb[:, 1],
"class": gold_standard_classes,
"k_means_clusters": clusters
},
index=data.columns.values)
write_csv(
embedding_df,
os.path.join(result_dir, "files",
"%s.csv" % embedding_slug))
info.append({
'filename':
"%s.csv" % embedding_slug,
'description':
'%s embedding of cells considering genes related '
'to cell-cycle' % embedding_name,
'plot_description':
'%s embedding of cells considering genes related '
'to cell-cycle (K-means clusters are marked '
'with different shapes)' % embedding_name,
})
metric_results.update({
'kmeans_on_%s_adjusted_mutual_info_score' % embedding_slug:
adjusted_mutual_info_score(gold_standard_classes,
clusters,
average_method="arithmetic"),
'kmeans_on_%s_v_measure_score' % embedding_slug:
v_measure_score(gold_standard_classes, clusters),
'embedding_%s_calinski_harabaz_score' % embedding_slug:
calinski_harabaz_score(emb, gold_standard_classes),
'embedding_%s_silhouette_score' % embedding_slug:
silhouette_score(emb, gold_standard_classes)
})
write_csv(pd.DataFrame(info),
os.path.join(result_dir, "files", "info.csv"))
result_path = os.path.join(result_dir, "result.txt")
with open(result_path, 'w') as file:
file.write("## METRICS:\n")
for metric in sorted(metric_results):
file.write("%s\t%4f\n" % (metric, metric_results[metric]))
file.write("##\n## ADDITIONAL INFO:\n")
file.write("## SVM classifiers accuracies: %s\n" %
str(svm_results))
file.write("## KNN classifiers accuracies: %s\n" %
str(knn_results))
log("Evaluation results saved to `%s`" % result_path)
if visualization != "none":
self.visualize_result(result_dir, output_type=visualization)
return metric_results